Using a genetic algorithm employing an expedited convergence mechanism to at least partially fit a medical implant to a patient using patient feedback

ABSTRACT

Apparatus and method for at least partially fitting a medical implant system to a patient is described. These apparatuses and methods comprise executing a genetic algorithm to select a set of parameter values for the medical implant system. This genetic algorithm may comprise generating successive generations of child populations until a confidence threshold is reached. This confidence threshold comprises determining whether the values of each parameter value to be selected have converged on particular value with a specified confidence level. In determining whether the values have converged, one or more initial generations of the search may be excluded in computing the likelihood that the value has converged.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation Application of U.S. application Ser.No. 12/557,233 filed Sep. 10, 2009, which is related to PCT ApplicationNo. PCT/US2004/007400, U.S. Pat. Nos. 4,532,930, 5,277,694, 6,123,660,6,162,169, 6,537,200, 6,565,503, 6,575,894, 6,697,674, 6,879,860, U.S.patent application Ser. No. 10/963,594, entitled “Using a GeneticAlgorithm to Fit a Cochlear implant System to a Patient,” filed Oct. 13,2004, U.S. patent application Ser. No. 12/557,208, entitled “Using aGenetic Algorithm to Fit a Medical Implant System to a Patient,” filedconcurrent with the present application, and U.S. patent applicationSer. No. 12/557,208, entitled “Using a Genetic Algorithm EmployingDynamic Mutation,” filed concurrent with the present application. Theentire disclosure and contents of the above patents are herebyincorporated by reference herein.

BACKGROUND

1. Field of the Invention

The present invention relates generally to stimulating medical devices,and more particularly, to fitting a stimulating medical device.

2. Related Art

Many medical devices have structural and/or functional features whichare to be adjusted for an individual patient. The process by which amedical device is tailored or customized for the specific needs of apatient is commonly referred to as fitting. One type of medical devicewhich is typically fitted to individual recipients is a cochlear implantsystem.

Cochlear implant systems provide the benefit of hearing to individualssuffering from severe to profound hearing loss. Hearing loss in suchindividuals is often due to the absence or destruction of the hair cellsin the cochlea which transduce acoustic signals into nerve impulses.Cochlear implant systems essentially stimulate the auditory nerves bydirectly delivering electrical stimulation to the auditory nerve fibers.This causes the brain to perceive a hearing sensation resembling thenatural hearing sensation normally delivered by the auditory nerve.Examples of cochlear implant systems are described, by way of example,in U.S. Pat. Nos. 4,532,930, 6,537,200, 6,565,503, 6,575,894, and6,697,674, among others.

Conventional cochlear implant systems commonly include an externalassembly directly or indirectly attached to the body of the patient(sometimes referred to herein as the recipient), and an internalassembly which is implanted in the patient. The external assemblytypically comprises one or more microphones for detecting sound, aspeech processing unit that converts detected sound into an electricalcoded signal, a power source, and an external transcutaneous transfercoil. The internal assembly typically comprises an internaltranscutaneous transfer coil, a stimulator unit located within a recessof the temporal bone of the recipient, and an electrode array positionedin the recipient's cochlea. Completely implantable cochlear implantsystems having functionally similar components are under development.

In addition to providing electrical stimulation, some cochlear implantsystems also include a mechanical stimulation mode of operation. Such socalled mixed-mode systems offer rehabilitation by mechanicallystimulating a portion of a patient's auditory pathway, eitheracoustically or physically. For example, there have been approaches tooffer rehabilitation with conventional hearing aids via the applicationof an amplified acoustic signal to the external auditory canal, or byphysically stimulating an ossicle of the middle ear or the inner ear viamechanical or hydromechanical stimulation.

Modern cochlear implant systems provide a wide variety of fittingoptions that can be customized for an individual patient. Becausepatients are heterogeneous, each patient requires a different set ofparameters to maximize speech reception and patient satisfaction. Thetask of the clinical professional, usually an audiologist, is to selecta set of parameters, commonly referred to as a parameter map or, moresimply, a MAP, that will provide the best possible sound reception foran individual patient. Because there may be thousands of possibleparameter maps, it is impractical for a patient to experience all of thealternatives and to evaluate the performance of each alternative. Nor isit possible to identify an optimal parameter map by prescription basedon a limited set of measurements as is, for example, the case in fittingeyeglasses. Because parameters of cochlear implant systems ofteninteract non-linearly and non-monotonically, it is also not possible tosequentially optimize parameters one at a time, adjusting each insuccession to its optimal value.

As a result, clinicians have adopted a variety of approaches for fittingthe cochlear implant systems to a patient. Some simply set theparameters to default values regardless of the individual patients.Others adopt preferred parameter maps, which they believe are good, ifnot best, for many or most patients. The preferences may be based onpersonal experience, published performance data, or intuition. Someclinicians evaluate a limited set of alternatives adjusting individualparameters based upon measured perceptual limitations and inferredrelationships among the parameters. These approaches are time consuming,costly, and unreliable, and typically fail to achieve the optimaloutcome for individual patients.

SUMMARY

In one aspect of the present invention there is provided a method for atleast partially fitting a medical implant system to a patientcomprising: executing a genetic algorithm to select a determined valueset comprising values for a plurality of fitting parameters, the geneticalgorithm comprising: presenting signals processed by a plurality ofvalue sets to the patient using the medical implant system; receivingpatient feedback in response to the presented signals processed by thevalue sets; selecting, based on the patient feedback, one or more of thevalue sets; computing a likelihood of convergence for at least one valueof the value set using values from a plurality of previously generatedgenerations and not using values from one or more of the previouslygenerated generations; generating a successive generation of value setsusing said selected one or more value sets; and repeating the steps ofpresenting, receiving, selecting, and generating until the computedlikelihood of convergence exceeds a confidence level; and providing saiddetermined value set to said medical implant system for use in providingstimulation to the patient.

In another aspect of the present invention, there is provided a systemfor at least partially fitting a medical implant system to a patientcomprising: a processor configured to execute a genetic algorithm toselect a determined value set comprising values for a plurality offitting parameters, wherein the processor in executing the geneticalgorithm is configured to present a plurality of value sets to thepatient using the medical implant system, receive patient feedback inresponse to the presented signals processed by the value sets, select,based on the patient feedback, one or more of the value sets, compute alikelihood of convergence for at least one value of the value set usingvalues from a plurality of previously generated generations and notusing values from one or more of the previously generated generations,generate a successive generation of value sets using said selected oneor more value sets; and repeat the steps of presenting, receiving,selecting, and generating until the computed likelihood of convergenceexceeds a confidence level; and an interface configured to provide atleast one of the value sets to the medical implant system for use by themedical implant system in providing stimulation to the patient.

In yet another aspect of the present invention, there is provided asystem for at least partially fitting a medical implant system to apatient comprising: means for executing a genetic algorithm to select adetermined value set comprising values for a plurality of fittingparameters, the genetic algorithm comprising: means for presentingsignals processed by a plurality of value sets to the patient using themedical implant system; means for receiving patient feedback in responseto the presented signals processed by the value sets; means forselecting, based on the patient feedback, one or more of the value sets;means for computing a likelihood of convergence for at least one valueof the value set using values from a plurality of previously generatedgenerations and not using values from one or more of the previouslygenerated generations; means for generating a successive generation ofvalue sets using said selected one or more value sets; and means forrepeating the steps of presenting, receiving, selecting, and generatinguntil the computed likelihood of convergence exceeds a confidence level;and means for providing said determined value set to said medicalimplant system for use in providing stimulation to the patient.

In yet another aspect there is provided a computer-readable mediaencoded with instructions operative to cause a computer to perform amethod for at least partially fitting a medical implant system to apatient, the method comprising: executing a genetic algorithm to selecta determined value set comprising values for a plurality of fittingparameters, the genetic algorithm comprising: presenting signalsprocessed by a plurality of value sets to the patient using the medicalimplant system; receiving patient feedback in response to the presentedsignals processed by the value sets; selecting, based on the patientfeedback, one or more of the value sets; computing a likelihood ofconvergence for at least one value of the value set using values from aplurality of previously generated generations and not using values fromone or more of the previously generated generations; generating asuccessive generation of value sets using said selected one or morevalue sets; and repeating the steps of presenting, receiving, selecting,and generating until the computed likelihood of convergence exceeds aconfidence level; and providing said determined value set to saidmedical implant system for use in providing stimulation to the patient.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention are described below with referenceto the attached drawings, in which:

FIG. 1 is a schematic diagram of an exemplary cochlear implant systemwhich may be fitted to an individual patient in accordance with anembodiment;

FIG. 2 is a schematic diagram illustrating one exemplary arrangement inwhich a recipient operated fitting system may be used in fitting astimulating medical device, in accordance with an embodiment;

FIG. 3 illustrates an exemplary MAP comprising a set of 8 binary genes,in accordance with an embodiment;

FIG. 4 is a high-level flow chart of an exemplary method for determininga MAP using a genetic algorithm, in accordance with an embodiment;

FIG. 5 illustrates one example of how an offspring MAP may inherit genesfrom each parent MAP, in accordance with an embodiment; and

FIG. 6 is a high-level flow chart illustrating operations that may beperformed for determining whether the search has converged to an optimalor near-optimal MAP, in accordance with an embodiment; and

FIG. 7 illustrates an exemplary GUI that may be provided to a recipientfor obtaining the recipients perception of applied stimulation, inaccordance with an embodiment.

DETAILED DESCRIPTION

Aspects of the present invention are generally directed to the use of agenetic algorithm in fitting a stimulating medical device. In certainembodiments, the fitting system uses an expedited convergence strategythat terminates the genetic algorithm search when the genetic algorithmis determined to have converged on a particular solution. Convergence ona particular solution may be determined by analyzing the statisticalhistory of all previously selected possible solutions, and determiningthat the solutions have converged when each value of the solutions canbe estimated with a specified confidence. In some embodiments, thepossible solutions are MAPs specifying parameter values for a subset ofparameters of the stimulating medical device.

Early generations of a genetic algorithm search are often deliberatelyrandom, and thus if included in a convergence calculation, may delay thedetermination that the genetic algorithm search has converged. In anembodiment, these early generations may be excluded in the determinationof whether the genetic algorithm has converged on a particular solution.However, the exact number of early generations that should be excludedmay vary among genetic algorithm searches. In an embodiment, everypossible number of excluded early generations may be analyzed indetermining whether the genetic algorithm search has converged on aparticular solution. Then, the genetic algorithm search may bedetermined when any of the possible exclusions of early generationsprovides a determination that the genetic algorithm search hasconverged. An exemplary embodiment of a genetic algorithm using anexpedited convergence calculation will be discussed in more detail belowafter a presentation of an exemplary environment for implementation ofsuch an algorithm.

FIG. 1 is a schematic diagram of an exemplary cochlear implant system100 which may be fitted to an individual patient in accordance withembodiments of the present invention. This embodiment of cochlearimplant system 100 has single- and mixed-mode operational capabilities.With regard to an electrical stimulation mode of operation, cochlearimplant system 100 provides direct electrical stimulation of auditorynerve cells to bypass absent or defective hair cells that normallytransduce acoustic vibrations into neural activity. In this illustrativeembodiment, cochlear implant system 100 comprises external componentassembly 142 which is directly or indirectly attached to the body of thepatient, and an internal component assembly 144 which is temporarily orpermanently implanted in a patient. External assembly 142 typicallycomprises at least one audio pickup device such as a microphone (notshown) for detecting sounds, a speech processing unit 116 that convertsthe detected sounds into a coded signal, a power source (not shown), andan external transmitter unit 106. External transmitter unit 106comprises an external coil 108, and, preferably, a magnet 110 secureddirectly or indirectly to external coil 108. Speech processor 116processes the output of the audio pickup devices that may be positioned,for example, by the ear 122 of the recipient. Speech processor 116generates stimulation signals which are provided to external transmitterunit 106 via cable 118.

Internal components 144 comprise an internal receiver unit 112, astimulator unit 126, and an electrode array 134. Internal receiver unit112 comprises an internal receiver coil 124 and a magnet 140 fixedrelative to internal coil 124. Internal receiver unit 112 and stimulatorunit 126 are hermetically sealed within a housing 128. Internal coil 124receives power and data from transmitter coil 108. A cable 130 extendsfrom stimulator unit 126 to cochlea 132 and terminates in an electrodearray 134. The received signals are applied by array 134 to basilarmembrane 136 thereby stimulating auditory nerve 138. Typically, theelectrodes differentially activate auditory neurons that normally encodedifferential pitches of sound.

Collectively, transmitter antenna coil 108 (or more generally, externalcoil 108) and receiver antenna coil 124 (or, more generally internalcoil 124) form an inductively-coupled coil system of a transcutaneoustransfer apparatus 102. Transmitter antenna coil 108 transmitselectrical signals to the implantable receiver coil 124 via a radiofrequency (RF) link 114. Internal coil 124 is typically a wire antennacoil comprised of at least one and preferably multiple turns ofelectrically insulated single-strand or multi-strand platinum or goldwire. The electrical insulation of internal coil 124 is provided by aflexible silicone molding (not shown). In use, implantable receiver unit112 may be positioned, for example, in a recess of the temporal boneadjacent ear 122 of the recipient.

Regarding the mechanical mode of operation, cochlear implant system 100provides, in this illustrative embodiment, direct mechanical stimulationto the patient's middle ear.

Electromechanical transducer 150 is coupled to the middle ear or innerear using any technique now or later developed. Transducer 150stimulates the impaired inner ear by direct mechanical coupling viacoupling element 152 to a middle ear ossicle or via an air gap couplingfor implantable transducers which are electromagnetic, for example. Inthis illustrative embodiment, electromechanical transducer 150 iscoupled to incus 140. One example of transducer 150 is described in U.S.Pat. No. 5,277,694 which is hereby incorporated by reference herein. Inthe embodiment of a hermetically tight transducer described therein, ahousing wall of the transducer is designed as a vibrating membranewhich, together with a piezoelectric ceramic wafer applied to the insidethereof, comprises an electromechanically active composite element, themechanical vibrations of which are transmitted to the ossicular chainvia a coupling rod 152 permanently attached to the outside of themembrane. Optionally, coupling rod 152 can be attached to the membranevia a coupling element which is connected to the coupling rod.Alternatively, transducer 150 can be implemented as described in U.S.Pat. No. 6,123,660 which is hereby incorporated by reference. In such anembodiment, a permanent magnet is attached to the inside of thepiezoelectric ceramic wafer to interact with an electromagnetic coil,such as an electromagnetic transducer. Such a combinedpiezoelectric-electromagnetic transducer is advantageous in particularwith respect to a wide frequency band and achieving relatively highvibration amplitudes with comparatively small supplied energy.

In an alternative embodiment, transducer 150 can be an electromagnetictransducer arrangement as is described in commonly owned U.S. Pat. No.6,162,169 which is hereby incorporated by reference herein. In such anembodiment, the transducer arrangement comprises a housing which can befixed at the implantation site with reference to the skull, with amechanically stiff coupling element which can move relative to thehousing. In the housing there is an electromechanical transducer whichto vibrates the coupling element.

The above signal processing components are controlled by amicrocontroller included, for example, in speech processor 116. Themicrocontroller includes a storage area in which patient-specificaudiologic adaptation parameters and the audiometry parameters of theabove-noted signal generator are stored. The microcontroller andassociated data storage may be implantable, such as within stimulatorunit 126. In such embodiments, the programmable data are sent to themicrocontroller via telemetry unit 102.

As noted, there may be a substantial number of parameters which may becustomized to optimally fit a cochlear implant system to an individualpatient. As will be described below, not all parameters may be selectedto obtain values with the genetic algorithm. The selected subset ofparameters and their respective values is collectively and generallyreferred to herein as a “parameter map,” a “cochlear map” or “MAP.” A“MAP” is also sometimes referred to as a “program.”

Examples of parameters include, for example, the speech strategyimplemented in the cochlear implant system. Additionally, within anygiven speech strategy a great many parameters may be specified to tailorthe encoding and stimulation for an individual patient. Examples ofparameters and parameter values that may be selected for a speechstrategy include but are not limited to the number of channels ofstimulation represented, the configuration and number of intracochlearand/or extracochlear electrodes which are to be associated with eachchannel, the pulse repetition rate for each channel, the pulse pattern,the width of each pulse or between pulses, the number of spectral maximaperiodically chosen for representation, the mapping of sound pressure tostimulus current for each channel (threshold levels, comfort levels andcompression curves), the frequency boundaries allocated for eachchannel, parameters for the front end filtering of the audio from themicrophone (pre-emphasis), and automatic gain control threshold,channel-specific compression ratios, and attack and release times.

In cochlear implant systems such as system 100 described above whichprovide electrical and mechanical stimulation, additional parameters maybe selected to tailor the cochlear implant system to an individualpatient. Such parameters include, but are not limited to, loudnessparameters such as long term loudness balance (that is, electrical andmechanical gains), parameters for short term gain manipulations,particularly signal-dependent gain adjustments. Such gain adjustmentsparameters include, for example, parameters for adjustments to minimizecross-modal masking, and adjustments to emphasize speech features suchas noise, frication or voicing.

Additional parameters may include frequency domain parameters. Suchparameters include, for example, overall frequency boundaries allocatedto electrical and mechanical stimulation, slopes of filtering at theboundaries of each stimulation signal, allocation of frequency subbands(both quantity and boundaries) in each domain, etc. Also, filtering ofthe mechanical signal within the passband, for example, to match thehearing loss or for other purposes.

Additional parameters may also include time domain parameters. Suchparameters include, for example, parameters for adjusting electricalperiodicity of pulse timing to be synchronized with the mechanicalsignal fluctuations, adjusting delays in the electrical stimulus tocompensate for missing propagation delays of various middle ear andinner ear pathways, etc.

Additional parameters may also include binaural parameters. Suchparameters include, for example, parameters for adjusting stimulustiming to preserve interaural timing cures, adjustment of stimulustiming to suppress echo, improve localization, or improve sound sourcesegregation, etc.

As used herein, the term ‘parameter values’ generally and collectivelyrefers to values of parameters, whether selectable options areprogrammed on or off, and in general to any choices that are made duringa fitting procedure. As one or ordinary skill in the art wouldappreciate, the above parameters are an example of mixed-mode parameterswhich may be selected and tailored to optimally fit a single-mode(electrical stimulation) and for mixed-mode (electrical and mechanicalstimulation) cochlear implant system to a patient.

FIG. 2 is a schematic diagram illustrating one exemplary arrangement 200in which a patient 202 operated fitting system 206 may be used infitting a stimulating medical device, such as a cochlear implant 100, inaccordance with an embodiment. In the embodiment illustrated in FIG. 2,sound processing unit 126 of cochlear implant 100 may be connecteddirectly to fitting system 206 to establish a data communication link208 between the sound processing unit 126 and fitting system 206.Fitting system 206 is thereafter bi-directionally coupled by means ofdata communication link 208 with sound processing unit 126. It should beappreciated that although sound processing unit 126 and fitting system206 are connected via a cable in FIG. 2, any communications link now orlater developed may be utilized to communicably couple the implant andfitting system.

Fitting system 206 may comprise a fitting system controller 212 as wellas a user interface 214. Controller 212 may be any type of devicecapable of executing instructions such as, for example, a general orspecial purpose computer, digital electronic circuitry, integratedcircuitry, specially designed ASICs (application specific integratedcircuits), firmware, software, and/or combinations thereof. Userinterface 214 may comprise a display 222 and an input interface 224.Display 222 may be, for example, any type of display device, such as,for example, those commonly used with computer systems. Input interface224 may be any type of interface capable of receiving information from apatient 202, such as, for example, a computer keyboard, mouse,voice-responsive software, touch-screen (e.g., integrated with display222), retinal control, joystick, and any other data entry or datapresentation formats now or later developed.

Fitting system 206 may be configured to perform a MAP search using agenetic algorithm. In performing the genetic algorithm search, fittingsystem 206 may represent the MAPs using a bit string comprising a set ofN_(b) ‘genes’ (bits), wherein the number of possible unique MAPs is2^(Nb). FIG. 3 illustrates an exemplary MAP 300 comprising a set of 8binary genes 304A-304H (N_(b)=8). Each of the 8 bits 304 may be used toindividually or collectively designate several parameters for cochlearimplant system 100.

In the example shown in FIG. 3, three such parameters 306A-306C aredesignated. Three bits 304A-304C are used to select a parameter 306A ofstimulus rate (the rate, in Hz, at which high-energy channels areselected and stimulus pulses are delivered to groups of N electrodes),three bits 304D-304F are used to select a parameter 306B of spectralmaxima counts (the number N of electrodes periodically selected to bestimulated, representing the N frequency bands with the highest energyat the time), and the remaining two bits 304G-304H select a parameter306C of the quantity of channels or frequency bands, used to representthe sound spectrum. Other parameters are assumed to be constant orderived from one of the three represented parameters 306.

It should be understood that FIG. 3 is exemplary only and provided toillustrate how a bit string may be used to represent various parametersof a MAP; and, in other embodiments the number of bits and what theyrepresent may be different in alternative embodiments. A furtherdescription of an exemplary mechanisms for representing MAPs using bitsstrings is provided in U.S. patent application Ser. No. 10/963,594entitled “Using a Genetic Algorithm to Fit a Cochlear Implant System toa Patient,”, the entire contents of which are hereby incorporated byreference.

FIG. 4 is a high-level flow chart of an exemplary method for determininga MAP using a genetic algorithm, in accordance with an embodiment. FIG.4 will be discussed with reference to the fitting system illustrated inFIG. 2.

A patient 202 may initiate the process by connecting cochlear implant100 to fitting system 206 at block 402. This may be accomplished byplugging a cable into the speech processor 126 of the cochlear implant100 and the fitting system 206. Or, for example, fitting system 206 andcochlear implant 100 may connect wirelessly in response to, for example,the patient 202 entering an instruction via user interface 214 thatinstructs fitting system 206 to wirelessly initiate a connection withcochlear implant 100. This connection may also cause the fitting systemto begin some initialization procedures. These initialization proceduresmay include a calibration step to help ensure that if the fitting system206 delivers a sound signal to the patient 202 via a speaker at aspecified loudness, the sound may be delivered with a constant soundlevel pressure to the sound processing unit 126 of the cochlear implant100 by the fitting system 206.

The fitting system 206 may generate an initial population of MAPs atblock 404. In an embodiment, the initial population may comprise 8selected MAPs. It should be noted that this number need not be 8, butmay vary depending on the implementation. Various techniques may be usedto select this initial generation of MAPs. For example, this selectionmay be performed by selecting at random from among the set of possibleMAPs. Or, for example, in order to insure greater diversity among theselected initial generation of MAPs, the MAPs may be selected so thatthe each parameter value is represented in at least one MAP. Forparameters in which the number of possible variables is greater than thenumber of MAPs in the initial generation, the values may be randomlyselected such that each MAP in the initial generation has a unique valuefor the parameter. For example, if there are 12 possible parametervalues, a first one of the parameter values may be randomly selectedfrom among the 12 possible values, and then when selecting the parametervalue for the second MAP the fitting system selects from amongst the 11remaining unselected values, and so on.

Or, for example, in an embodiment, in order to insure that this initialMAP set has a sufficient measure of heterogeneity, its diversity maycomputed. In one embodiment, diversity is defined as the average Hammingdistance between the various MAPs, and it ranges between 0 and 1, with 1indicating maximum diversity and 0 indicating minimum diversity. If thediversity is below a threshold, for example, 0.53, then the initialgeneration has an insufficient diversity, and the fitting system 206 mayselect a new set of MAPs. Moreover, pre-selected MAPs may also beincluded among the MAPs of the first generation. These pre-selected MAPsmay be drawn, for example, from prior runs of the fitting procedure,MAPs stored in a memory of fitting system 206, or MAPs selected by aclinician based on his experience, suggestions and recommendations fromothers, etc. This initial population of MAPs may be referred to as thefirst generation of MAPs.

At block 406, audible signals processed by the population of MAPs arepresented to the patient. This may involve fitting system 206sequentially providing each MAP to the patient's cochlear implant 100and then presenting an audible signal that that is processed by theprovided MAP. The audible signal may be a sound token that comprises anytype of sound, such as a single speaker reading aloud from a newspaperor a passage from an audio book, a particular piece of music, or portionof same, a musical instrument, a car horn, etc. The audible signals(e.g., sound tokens) may be stored in a file contained in a library ofaudible signals that is stored, for example, in fitting system 206 or inan external storage device connected to fitting system 206.

In an embodiment, fitting system 206 may randomly select a sound tokenfor each of the 8 MAPs. Fitting system 206 may then download the MAPcorresponding to a particular sound token to the cochlear implant, andthen play the audible signal (e.g., the sound token). This audiblesignal may be played using, for example, one or more speakers connectedto fitting system 206, such as, for example, a set of headphones worn bythe patient 202. Further, patient 202 and fitting system 202 may belocated in a room such as, for example, a sound proof room, designed tominimize external noise interfering with the presentation of the audiblesignal. Or, for example, fitting system 206 may provide the audiblesignal directly to cochlear implant 100 via data communications link208. After fitting system 206 presents the first sound token to thepatient 202, the fitting system 206 may download the next MAP to thecochlear implant 100 and then play the corresponding sound token to thepatient 202. This process may then continue sequentially until fittingsystem 206 presents sound tokens processed by all 8 MAPs to the patient202.

After fitting system 206 presents each of the sound tokens andcorresponding MAPs to the patient 202, fitting system 206 may thenreceive an indication from the patient 202 regarding which sound tokenswere perceived as good by the patient 202. Using this indication,fitting system 206 may determine the parents for the next generation ofMAPs at block 408. In an embodiment, all MAPs identified as good by thepatient 202 may be selected as parent MAPs for generating the nextgeneration of MAPs. Or, for example, the patient 202 may select the 4MAPs that the patient considered to be the best MAPs and these 4 bestMAPs are selected as the parent MAPs.

In yet another embodiment, the patient may be presented with a soundtoken comprising a statement, and the patient asked to identify whatthey heard. Then, if the patient 202 correctly identified what theyheard, the corresponding MAP may be used as a parent MAP generating thenext generation of MAPs. A further description of user interface thatmay be used to perform such an objective test in identifying good MAPsis presented below with reference to FIG. 7.

It should be noted that these are but some exemplary mechanisms that maybe used at blocks 406 and 408 for selecting parent MAPs from thepopulation of MAPs, and other mechanisms may be used without departingfrom the present invention. For example, mechanisms for selecting parentMAPs may be used such as described in U.S. patent application Ser. No.12/557,208, entitled “Using a Genetic Algorithm to Fit a Medical ImplantSystem to a Patient,” the entire contents of which are herebyincorporated by reference. Further, it should be noted that althoughblocks 406 and 408 are illustrated as separate blocks, this was done forillustrated purposes, and in embodiments they may be combined or involvean iterative process where the two blocks are repeated until a certaincondition occurs, such as all MAPs are presented or a particular numberof MAPs are selected.

Fitting system 206 may then check to see if a confidence threshold hasbeen reached at block 410 for the selected MAPs. In an embodiment,fitting system 206 may determine that the confidence threshold has beenreached if the value of each bit of the MAP bit string is considered tohave converged and its value is known with a particular confidence levelbased on the historical distribution of that bit. For instance, bit #1may be considered converged if it has historically either been either a“1” or a “0” a particular percentage of the time (e.g., 95% of the timeaccording to a binomial distribution) over its entire history. As such,probability dictates that there is a 95% chance that this bit will bethe same value again in a subsequent generation. When all bits reachthis confidence level (e.g., 95%), fitting system 206 may determine thatthe confidence threshold has been met. In other words, in this example,the fitting system 206 determines that the confidence threshold has beenmet if the confidence level for each of the bits exceeds the specifiedconfidence level (e.g., 95%).

An embodiment uses an expedited convergence algorithm that may analyse amultitude of historical sequences of generations from every possibleinitial generation through the current/latest generation. Computingconfidence levels using, in an embodiment, every possible historicalsequence of generations may help expedite the convergence of thealgorithm. An exemplary mechanism for such an expedited convergencealgorithm is discussed below with reference to FIG. 6.

If at block 410 the confidence threshold has not been achieved, fittingsystem 206 may determine if the maximum number of generations has beenreached at block 416. If so, fitting system 206 may proceed to block 410to select the final MAP. Otherwise the algorithm continues to block 418.

Fitting system 206 may determine if the maximum number of generationshas been reached by maintaining a tally of each pass through block 416and then comparing this tally against a particular value specifying themaximum number of generations to be used in determining the final MAP.For example, in an embodiment, fitting system 206 may set a generationnumber variable, G, equal to one (G=1) at block 404, and then increase Gby 1 (G=G+1) at block 418 or 420. It should be noted that this is butone mechanism in which fitting system 206 may maintain a generationnumber variable G that is equal to the current generation number of thegenetic algorithm search, and other mechanisms may be employed.

Fitting system may then generate child MAPs at block 418. Varioustechniques may be used for generating the child MAPs. For example, in anembodiment, a child MAP may be generated by pairing two of the parentMAPs selected at block, selecting a cut point, and then generating twochildren MAPs from the selected parents. In an embodiment, the genes fora child MAP to the left of the cut point may come from one parent, andthe genes to the right of the cut point may come from the other parent.

FIG. 5 illustrates one example of how an offspring MAP may inherit genesfrom each parent MAP, in accordance with an embodiment. In theillustrated example, each parent MAP 502A and 502B is represented by an8 bit string. Fitting system 206 may select a cut point 510 for the bitstring of each parent MAP 502A and 502B. The selected cut point 510 maybe between any particular bits of the bit strings, and in FIG. 5 the cutpoint 510 is selected in the middle of each bit string. Further, in theillustrative example, the cut point is allowed to vary randomly acrosspairings. For example, in an embodiment, the fitting system 206 mayanalyze the two parent MAPs to identify bits that are different betweenthe parents, and then randomly select a cut point between the bitsidentified to be different. Alternatively, the cut points can be made inthe same position for all the pairings.

Then, fitting system 206 may generate two child MAPs 504A and 504B fromthe parent MAPs 502, where the first child MAP 504A includes bits ofparent MAP 502A to the left of the cut point 510 (i.e., the first 4bits) and the of the parent MAP 502B to the right of the cut point 510(i.e., the last 4 bits). Similarly, the genes of the second child MAP504B include the first 4 bits of parent MAP 502B and the last 4 bits ofparent MAP 502A. It should be noted that this is but one example of howchild MAPs may be generated from parent MAPs, and other mechanisms maybe used.

Fitting system 206 may then select at block 420 the child MAPs for thenext generation of MAPs to be presented to the patient (i.e., for whichsound tokens are to be presented to patient 202 processed by the MAPs).Various techniques may be used for selecting the new generation of MAPs,such as those discussed in the above-referenced U.S. patent applicationSer. No. 12/557,208, entitled “Using a Genetic Algorithm to Fit aMedical Implant System to a Patient.” For example, if fitting system 206generates an overloaded generation at block 416, fitting system 206 mayrandomly select at block 418 a particular number (e.g., 8) of MAPs fromthis pool of child MAPs. Then, fitting system 206 may use these 8randomly selected MAPs as the MAPs for the next generation of MAPs forwhich audible signals presented to the patient 202 at block 406 will beprocessed.

Fitting system 206 may then return to block 406 and present audiblesignals processed by this next generation of MAPs to the patient. Theprocess may then continue until either the confidence threshold isexceeded at block 410 or the maximum number of generations is reached atblock 414.

Once the confidence threshold or maximum number of generations has beenreached, fitting system 206 may then determine, at block 412, theparticular MAP to be used by the cochlear implant 100. Varioustechniques may be used for determining the final MAP depending, forexample, on how the process was terminated. For example, if theconfidence threshold was reached by a determination that each bit of theMAP is known with a confidence level exceeding the specified confidencelevel (e.g., 95%), then fitting system 206 may simply use these knownbit values for the final MAP. The term final MAP is used in thisdescription to refer to the MAP that is determined by the geneticalgorithm and subsequently downloaded to the cochlear implant 100 foruse after fitting the implant. However, it should be noted that this MAPmay be changed after fitting, such as, for example, by the patient 202re-fitting the cochlear implant 100 at a later time.

If, however, block 406 is reached due to, for example, the maximumnumber of generations being reached or a determination that theconfidence threshold was exceeded by some other mechanism, fittingsystem 206 may use various other mechanisms for determining the finalMAP. For example, fitting system 206 may present audible signalsprocessed by each of the final selected MAPs to the patient 202 andreceive an indication from the patient regarding which MAP is consideredthe best MAP. Or, for example, fitting system 206 during the process maystore an indication regarding each MAP that is selected at block 408through each iteration, then fitting system 206 may select the MAP thatwas selected by the patient 202 the most number of times at block 408and use this MAP as the final MAP.

Or, in yet another example, fitting system 206 may select a number(e.g., 4) of MAPs that were selected by the patient 202 at block 408 themost number of times and present audible signals processed by theseselected MAPs (referred to as the final round) to the patient 202. Inpresenting audible signals processed by the final round of MAPs to thepatient, fitting system 206 may provide multiple rounds ofpresentations, where each MAP is presented to the patient 202 in eachround using a different sound token. The patient 202 may then select the“best” MAP for each round, and then after a particular number of rounds(e.g., 4 rounds), the MAP that was selected as best the most amount oftimes is determined to be the final MAP. If after 4 rounds, there is atie, a series of one or more rounds may be run for the top tied MAPs todetermine the final MAP.

After the final MAP is determined at block 412, fitting system maydownload this determined MAP to cochlear implant 100 for subsequent useat block 414. In addition, fitting system 206 may store this final MAPalong with information identifying the patient 202 and/or cochlearimplant 100 in a storage of fitting system 206. Thus, in the event thepatient's cochlear implant 100 becomes corrupted, fitting system 100 maybe able to more quickly re-provide the determined MAP to cochlearimplant. Or, for example, if the genetic algorithm search is repeatedfor the patient at a later date, an audiologist may be able to view andcompare the newly determined MAPs as well as previously determined MAPsfor the patient, or even use the previously determined MAP(s) as one ofthe MAP in the initial generation of the genetic algorithm search.

FIG. 6 is a high-level flow chart illustrating operations that may beperformed for determining whether the confidence threshold of the searchhas been reached, in accordance with an embodiment. FIG. 6 will bediscussed with reference to decision 410 of FIG. 4 and the fittingsystem illustrated in FIG. 2. Further, in this exemplary method, theMAPs are represented by bit maps. This exemplary expedited convergencemethod allows for the fitting system 206 to exclude early generations indetermining whether the bits have converged on a final value.

Initially, fitting system 206 may set at block 602 a counter variable,i, to be equal to one less than the current generation number, G, (i.e.,i=G−1). Fitting system 206 may also set a bit counter variable, k, asequal to 1 (k=1) at block 404. Fitting system 206 may then at block 606compute the likelihood that the kth bit of the MAP is a 1 or a 0 usinggenerations i through the current generation, G. The likelihood that thevalue of a bit is known is referred to herein as a likelihood ofconvergence.

In an embodiment, fitting system 206 may compute the likelihood that thekth bit is a 1 or 0 by counting the number of times the kth bit is a 1in each MAP in generations i through G. For example, in an embodiment,fitting system 206 may store each MAP selected at block 408 along withthe generation number when the MAP was selected. Then, fitting system206 may retrieve all the stored MAPs with a generation number greaterthan or equal to i, and count the number of times the kth bit of theretrieved MAPs is a 1 and the number of times the kth bit is a 0.Fitting system 206 may then divide each count by the number of MAPscounted in generations i through G to provide the probability that thekth bit is a 1 or a 0. That is, the likelihood the kth bit is a 1 iscomputed by dividing the number of times the kth bit is equal to a 1 ingenerations i though G by the number of MAPs in generations i though G.And, the likelihood the kth bit is a 0 is computed by dividing thenumber of times the kth bit is equal to a 0 in generations i though G bythe number of MAPs in generations i though G. It should be noted thatthis is but one example of a mechanism for computing the likelihood thatthe kth bit is a 1 or a 0 and other mechanisms may be used. For example,in computing the likelihood, fitting system 206 may use all child MAPsgenerated at block 418 or, all child MAPs selected at block 420 incomputing the likelihood that the kth bit is a 1 or a 0.

After computing the likelihood that the kth bit is a 1 or a 0, fittingsystem 206 may determine whether either likelihood is greater than aparticular confidence level at decision 608. This confidence level maybe set to any desired value, and in an embodiment, a bit is consideredconverged when it is known with a confidence level greater than or equalto 95%. However, in other embodiments, other confidence levels may beused (e.g., 90%, 99%, etc.).

If the computed likelihood of the kth bit being a particular valueexceeds the confidence level, the bit variable, k, is increased by 1 atblock 612. Then, fitting system 206 may determine at decision 612,whether there are additional bits to be checked. That is, fitting system206 may check to see if k is less than or equal to the number of bits,N, in each MAP.

If there are additional bits to check, fitting system 206 returns toblock 606 and determines if the value of this kth bit is known with alikelihood exceeding the specified confidence level. The process thencontinues repeats blocks 606 to 612 for each of the N bits of the MAP.

If the value of each bit is known with a likelihood exceeding thespecified confidence level (e.g., 95%), the MAP is then saved and theprocess is considered to have converged on the optimum MAP. Thus,referring back to FIG. 4, the fitting system 206 may consider thatconfidence threshold has been exceeded at block 410. In other words, inan embodiment, fitting system 206 may determine that the confidencethreshold has been met if the likelihood of convergence for each bit inthe MAP exceeds the confidence level (e.g., 95%).

Referring back to decision 608, if fitting system 206 determines thatany bit is not known with a likelihood exceeding the confidence level,the process may proceed to block 620 and the value of i reduced by 1(i=i−1). This has the effect of increasing the number of generations(i.e., generations i through G) that will be used in block 606 forcomputing the likelihood that each bit is a 1 or a 0.

Next, fitting system 206 may check at decision 622 whether the value ofi is less than a threshold level. In an embodiment, this threshold levelmay be set to 0, indicating that the last computation of the likelihoodthat each bit is known with a particular confidence will be completedusing the MAPs for all generations (i.e., including the initialgeneration of MAPs up through the current generation of MAPs). However,in other embodiments, other threshold levels may be used, such as forexample, a threshold level, T, equal to a percentage of G (e.g.,T=∥G/2∥)).

If fitting system 206 determines that i remains above the thresholdlevel, T, the process may return to block 604 and fitting system 206 maythen determine using generations, i through G, whether the likelihood ofeach bit of the MAPs is known with the specified confidence level.

If however, fitting system 206 determines that i is less than or equalto the threshold level, T, the process is terminated at block 630.Referring back to decision 410 of FIG. 4, fitting system 206 may thendetermine that the confidence threshold has not yet been reached and thegenetic algorithm search should be continued.

Although the exemplary expedite convergence algorithm of FIG. 6 wasdiscussed above with reference to bit string representations of theMAPs, in other embodiments the method of FIG. 6 may be adapted for MAPsusing by data structures in which the actual parameter values arestored. In such an example, the fitting system 206 may determine that aparticular parameter has converged if the likelihood that the parameterhas any particular value exceeds a particular confidence level (e.g.,95%). Further, as with the embodiment of FIG. 6, early generations maybe excluded in computing this likelihood. A further description of MAPsusing data structures in which the actual parameter values are stored isprovided in the above-referenced U.S. patent application Ser. No.12/557,208, entitled “Using a Genetic Algorithm to Fit a Medical ImplantSystem to a Patient.”

Referring back to block 408 of FIG. 4, fitting system 206 may usevarious mechanisms for parent selection. For example, as discussedabove, at block 406, the patient 202 may sequentially listen to a soundtoken processed by each of the MAPs, where the fitting system plays aunique sound token for each provided MAP. Then, the patient 202 maydetermine which of the audible signals and corresponding MAPs soundedthe clearest. For example, in an embodiment, fitting system 202 mayprovide the patient with 8 MAPs at block 406 and the patient may thenselect the 4 MAPs that sounded the best to the patient. Or, in anotherembodiment, the patient 202 need not select a particular number of MAPsthat sounded the clearest, but instead, may simply identify the providedMAPs that the patient considered good. A further discussion of exemplarymechanisms for a patient 202 to select a variable number of MAPs isprovided in the above-referenced U.S. patent application Ser. No.12/557,208, entitled “Using a Genetic Algorithm to Fit a Medical ImplantSystem to a Patient.”

In yet another embodiment, rather than the fitting system 206 asking thepatient 202 whether the sound perception was good or not, the fittingsystem 206 may ask the patient 202 what they heard. And, then fittingsystem 206 may identify the corresponding MAP as good if the patient 202correctly identifies the sound token. For example, in an embodiment, thesound tokens may comprise spoken phrases. Then, fitting system 206 maypresent a graphical user interface (GUI) to the patient 202 via display222 that lets the patient 202 select a graphical list of possiblephrases. If the patient 202 selects the correct phrase, thecorresponding MAP may be deemed good, while if the incorrect phrase wasselected, the corresponding MAP may be deemed bad. That is, if thecorrect phrase is identified, fitting system 206 may select thecorresponding MAP at block 408. While, if the correct phrase is notidentified, fitting system 206 may not select the corresponding MAP atblock 408. In the event more than the number of desired MAPs (e.g., 4)is identified as good by the patient 202, fitting system 206 may cullthe MAPs to reduce the number of selected MAPs as good. And, if lessthan the number of desired number of MAPs (e.g., 4) is selected, fittingsystem 206 may randomly select new MAPs from the population of MAPs toact as new parents. It should be noted that these are but some examplesof mechanisms that may be used to obtain the desired number of MAPs atblock 408 and other mechanisms may be used.

FIG. 7 illustrates an exemplary GUI 700 that may be provided to arecipient for obtaining the recipients perception of appliedstimulation, in accordance with an embodiment. As illustrated, GUI 700may comprise a set of icons 702 that the recipient may select toindicate which phrase they believe they heard. For example, these icons702 may include an icon for selecting that the recipient heard any oneof eight phrases 702A-H. This GUI 700 may be displayed on display 222.The recipient may, using input interface 224, select the icon 702corresponding to the phrase they believe they heard. The input interface224 may then provide this response to fitting system controller 212.

Additionally, GUI 700 may include a play icon 704 that the recipient mayselect to direct the fitting system controller 212 to play the phrasethat the patient 202 is to identify. The GUI 700 may also comprise astop button 706 that the patient 202 may select to stop the process,such as if the recipient needs to leave for any purpose.

It should be noted that GUI 700 is exemplary only and provided toillustrate one example of a GUI interface that may be used fordetermining whether a patient can correctly identify a sound tokenprocessed by a particular MAP. For example, in other embodiments, thesound token may be a sound from a musical instrument and the icons thatthe recipient may select may be in the shape of different instrumentsand/or be identified by the name of a particular instrument. Further, inother embodiments, the patient 202 may be able to use check boxes,pull-downs, or other mechanisms for identifying a particular sound.

In an embodiment in which the patient 202 is asked to correctly identifya particular sound, the fitting system 206 may sequentially presentaudible signals processed by a predetermined number of MAPs (e.g., 8) tothe patient each processing a different sound token. Then, the MAPscorresponding to sounds that the patient correctly identified are deemedby the fitting system 206 as good and accordingly selected at block 408.As such, in this example, the number of MAPs selected at block 408 mayvary between 0 and all of the presented MAPs (i.e., MAPs used to processaudible signals presented to the patient at block 406). In the event noMAPs are selected at block 408, the process may return to block 406 andthe previously set of MAPs may be re-presented to the patient 202,although this time with a new set of sound tokens. If, however, no MAPsare again selected at block 408, the process may be re-started andreturned to block 404.

In yet another embodiment, the steps of blocks 406 and 408 may berepeated until the patient correctly identifies a particular number ofsound tokens (e.g., 4). Then, fitting system 206 may select as parentsthe MAPs corresponding to the correctly identified sound tokens. Forexample, in an embodiment, fitting system 418 may randomly select achild MAP from the child MAPs generated at block 418 and then present asound token processed by the selected MAP to the patient 202. Thefitting system 202 may then ask the patient 202 to correctly identify asound token processed using the MAP. After which, fitting system 206 mayrandomly select another child MAP and present a sound token processed bythis MAP to the patient. This process may the continue until the patientcorrectly identifies a particular number of sound tokens (e.g., 4). Thefitting system 206 may then use the MAPs corresponding to the correctlyidentified sound tokens as the parent MAPs selected at block 408.

In certain embodiments, certain sound tokens may work better with otherMAPs or certain sound tokens may be simply difficult for the patient tounderstand regardless of the MAP used to process the sound token. Forexample, if a sound token comprises speech from a person with a strongsouthern accent, but the patient 202 speaks with a strong Englishaccent, the sound token may be difficult for the patient to identifyregardless of the MAP. Thus, it is possible that good MAPs may not beselected at block 408 if they are paired with a bad sound token. Inorder to mitigate the impact of bad sound tokens, in an embodiment,blocks 406 and 408 may be repeated two or more times. For example, in afirst pass through blocks 406 and 408, each MAP may be paired with aparticular sound token. Then, during the second pass, the same MAPs andsound tokens may be used, but with different sound tokens matched upwith different MAPs. For example, in an embodiment, in the second pass,the MAPs considered bad in the first pass may switch sound tokens withthe MAPs considered good in the first pass. As an illustrative example,in a first pass MAP “A” processes token “1,” “B” processes token “2,”“C” processes token “3”, “D” processes token “4”, “E” processes token“5,” “F” processes token “6,” “G” processes token “7,” and “H,”processes token “8.” The, if MAPs A, B, C, and D are selected, in thenext pass through blocks 406 and 408 MAP A may be paired with token 5, Bwith token 6, C with token 7, D with token 8, E with token 1, F withtoken 2, G with token 3, and H with token 4. Then, at block 408, in thesecond pass through, fitting system 206 may identify any MAP selected ineither pass as good and use these MAPs as parents for generating thenext generation.

In yet another embodiment, the genetic algorithm may use two or morepopulations, rather than a single larger population. For example, thegenetic algorithm process may be performed two or more times with adifferent set of MAP parameters for each genetic algorithm search. Inone such example, in the first genetic algorithm search, the parametersrepresented by the MAP may be more critical parameters. Then in thesecond genetic algorithm search, the parameters determined in the firstsearch will be fixed and the second genetic algorithm search used toidentify less critical parameters. For example, in an embodiment, afirst genetic algorithm search may be performed using MAPs that specifythe stimulation rate, number of electrodes, and number of Maxima to beused. Other parameters may be set to default values during this firstsearch. Then, the parameter values determined during this first geneticalgorithm search are fixed, and the second genetic algorithm search maybe performed using these previously determined values as fixed terms.The MAP parameters used in the second genetic algorithm may includeparameters, such as values for the type of and shape of filters, etc. Inother words, the fitting system 206 may sequentially perform multiplegenetic algorithm searches where values for different subsets ofparameters are identified in each search, and the values identified inpreviously performed searches are used as fixed values in subsequentgenetic algorithm searches.

The above discussed processes, such as FIGS. 4 and 6, and mechanisms maybe embodied on software executable by a computer. Additionally, in anembodiment fitting system 206 may be a patient's home computer, personaldigital assistant (PDA) or other device on which such software isloaded. Additionally, in such an embodiment, a piece of hardware may beused for allowing the patient's computer to communicate with thepatient's cochlear implant for the purposes of, for example, changingthe MAP used by the patient's cochlear implant. This hardware may beconnected to the patient's computer, PDA, etc. using for example, as USBinterface, a firewire interface or any other suitable mechanism. Or, forexample, the patient's computer, PDA, etc. may communicate wirelesslywith the patient's cochlear implant using Wi-Fi, Bluetooth, or any othersuitable wireless interface included in the computer and cochlearimplant. Additionally, in embodiments, the patient could performoptimizations at using signals of his or her own choosing (e.g., aspouse's voice, a musical piece, etc.) or simply using the microphoneinput of the stimulating medical device. In such embodiment, thesoftware may provide the patient with the ability to upload and storethese audible signals for use by the genetic algorithm.

It should be noted that although the above-discussed embodiments werediscussed with reference to a cochlear implant, in other embodiments afitting system may be used to permit a patient 202 to measure thedynamic range of other stimulating medical devices, such as, forexample, bone conduction devices, auditory brain stimulators, etc.

Various implementations of the subject matter described, such as theembodiment of FIG. 2, components of may be realized in digitalelectronic circuitry, integrated circuitry, specially designed ASICs(application specific integrated circuits), computer hardware, firmware,software, and/or combinations thereof. These various implementations mayinclude implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which may be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device

These computer programs (also known as programs, software, softwareapplications, applications, components, or code) include machineinstructions for a programmable processor, and may be implemented in ahigh-level procedural and/or object-oriented programming language,and/or in assembly/machine language. As used herein, the term“machine-readable medium” refers to any computer program product,computer-readable medium, apparatus and/or device (e.g., magnetic discs,optical disks, memory, Programmable Logic Devices (PLDs)) used toprovide machine instructions and/or data to a programmable processor,including a machine-readable medium that receives machine instructionsas a machine-readable signal. Similarly, systems are also describedherein that may include a processor and a memory coupled to theprocessor. The memory may include one or more programs that cause theprocessor to perform one or more of the operations described herein.

All documents, patents, journal articles and other materials cited inthe present application are hereby incorporated by reference.

Embodiments of the present invention have been described with referenceto several aspects of the present invention. It would be appreciatedthat embodiments described in the context of one aspect may be used inother aspects without departing from the scope of the present invention.

Although the present invention has been fully described in conjunctionwith several embodiments thereof with reference to the accompanyingdrawings, it is to be understood that various changes and modificationsmay be apparent to those skilled in the art. Such changes andmodifications are to be understood as included within the scope of thepresent invention as defined by the appended claims, unless they departthere from.

What is claimed is:
 1. A method for at least partially fitting a medicalimplant system to a patient comprising: presenting signals processedaccording to a current generation of value sets for a plurality offitting parameters to the patient using the medical implant system;receiving patient feedback in response to the presented signals;executing a genetic algorithm to generate a next generation of the valuesets, the genetic algorithm including: selecting, based on the feedback,one or more of the value sets from amongst the current generation;computing a likelihood of convergence for at least one value of eachselected value set using values from newer generations while selectivelyexcluding values from one or more older generations; generating a nextgeneration of value sets using the selected one or more value sets;treating the next generation as the current generation; and repeatingthe steps of presenting, receiving and executing until the likelihood ofconvergence exceeds a confidence level; and providing, once theconfidence level has been exceeded, a selected one of the currentgeneration of value sets to the medical implant system.
 2. The method ofclaim 1, wherein: the likelihood of convergence for each value is aprobability that the value equals a corresponding reference value. 3.The method of claim 1, wherein: the executing a genetic algorithmfurther includes: setting a current size for a group of generations; andpopulating the group of generations using instances of newer generationswhile excluding one or more instances of older generations; and thecomputing a likelihood of convergence uses values from the group ofgenerations.
 4. The method of claim 3, wherein: one of the instances ofnewer generations is the current generation.
 5. The method of claim 3,wherein: the executing a genetic algorithm further includes: setting, ifthe confidence level has not been exceeded, a next size for the group ofgenerations, the next size being larger than the current size;resetting, if the confidence level has not been exceeded, the currentsize to that of the next size; iterating the steps of computing, settingand resetting until the confidence level has been exceeded.
 6. Themethod of claim 5, wherein: the iterating incrementally addsprogressively older generations to the group of generations.
 7. Themethod of claim 1, wherein: the computing a likelihood of convergencecomputes for each value of each selected value set.
 8. The method ofclaim 1, wherein: the medical implant system is a cochlear implantsystem; and the presented signals are audible signals.
 9. A system forat least partially fitting a medical implant system to a patientcomprising: an interface configured to: present, via the medical implantsystem, signals processed according to a current generation of valuesets for a plurality of fitting parameters to the patient; and receive,via the medical implant system, patient feedback in response to thepresented signals; a processor configured to: execute a geneticalgorithm to generate a next generation of the value sets, the geneticalgorithm including: selecting, based on the feedback, one or more ofthe value sets from amongst the current generation; computing alikelihood of convergence for at least one value of each selected valueset using values from newer generations while selectively excludingvalues from one or more older generations; generating a next generationof value sets using the selected one or more value sets; treating thenext generation as the current generation; and repeatedly present theprocessed signals, receive the feedback and execute the geneticalgorithm until the likelihood of convergence exceeds a confidencelevel; and an interface configured to provide, once the confidence levelhas been exceeded, a selected one of the current generation of valuesets to the medical implant system.
 10. The system of claim 9, wherein:the likelihood of convergence for each value is a probability that thevalue equals a corresponding reference value.
 11. The system of claim 9,wherein: regarding the execution of the genetic algorithm, the processorbeing configured to: set a current size for a group of generations; andpopulate the group of generations using instances of newer generationswhile excluding one or more instances of older generations; andregarding the computation of a likelihood of convergence, the processoris further configured to use values from the group of generations. 12.The system of claim 11, wherein: one of the instances of newergenerations is the current generation.
 13. The system of claim 11,wherein: regarding the execution of the genetic algorithm, the processorbeing configured to: set, if the confidence level has not been exceeded,a next size for the group of generations, the next size being largerthan the current size; reset, if the confidence level has not beenexceeded, the current size to that of the next size; iterate thecomputing, setting and resetting until the confidence level has beenexceeded.
 14. The system of claim 13, wherein: regarding the iteration,the processor is further configured to incrementally add progressivelyolder generations to the group of generations.
 15. The system of claim9, wherein: regarding the computation of a likelihood of convergence,the processor is further configured to compute for each value of eachselected value set.
 16. The system of claim 9, wherein: the medicalimplant system is a cochlear implant system; and the presented signalsare audible signals.
 17. A system for at least partially fitting amedical implant system to a patient comprising: presenting means forpresenting signals processed according to a current generation of valuesets for a plurality of fitting parameters to the patient using themedical implant system; receiving means for receiving patient feedbackin response to the presented signals; execution means for executing agenetic algorithm to generate a next generation of the value sets, thegenetic algorithm including: means for selecting, based on the feedback,one or more of the value sets from amongst the current generation; meansfor computing a likelihood of convergence for at least one value of eachselected value set using values from newer generations while selectivelyexcluding values from one or more older generations; means forgenerating a next generation of value sets using the selected one ormore value sets; means for treating the next generation as the currentgeneration; and means for repeatedly operating the presenting means,receiving means and execution means until the likelihood of convergenceexceeds a confidence level; and means for providing, once the confidencelevel has been exceeded, a selected one of the current generation ofvalue sets to the medical implant system.
 18. A non-transitorycomputer-readable medium encoded with instructions operative to cause acomputer to perform a method for at least partially fitting a medicalimplant system to a patient, the method comprising: presenting signalsprocessed according to a current generation of value sets for aplurality of fitting parameters to the patient using the medical implantsystem; receiving patient feedback in response to the presented signals;executing a genetic algorithm to generate a next generation of the valuesets, the genetic algorithm including: selecting, based on the feedback,one or more of the value sets from amongst the current generation;computing a likelihood of convergence for at least one value of eachselected value set using values from newer generations while selectivelyexcluding values from one or more older generations; generating a nextgeneration of value sets using the selected one or more value sets;treating the next generation as the current generation; and repeatingthe steps of presenting, receiving and executing until the likelihood ofconvergence exceeds a confidence level; and providing, once theconfidence level has been exceeded, a selected one of the currentgeneration of value sets to the medical implant system.